Hardware systems and distributed deep learning are growing fast. This considerable growth makes the progress of distributed DRL inevitable. Therefore, we believe deploying distributed DRL techniques would be beneficial and efficient for every researcher with any computational resources. Recently, [1]} developed Agent57, the first DRL agent to obtain an above-the-human baseline score on all 57 Atari 2600 games. Different reinforcement learning algorithms and modifications are used Agent57, from distributed architecture to meta-controller for efficient exploration. Figure REF  illustrates the performances of some single-actor and some distributed architectures on the top 5\(\%\)  struggling games in Atari57 (The best one is Agent57). We think it is still an open and increasingly significant research area to scale-up DRL.
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